Publications

• The combination of autism and exceptional cognitive ability is associated with suicidal ideation. Casten, L., Thomas, T., Doobay, A., Foley-Nicpon, M., Kramer, S., Nickl-Jockschat, T., Abel, T., Assouline, S., and Michaelson, J. (2023). Neurobiology of Learning and Memory, Volume 197.
• Estimating the prevalence and genetic risk mechanisms of ARFID in a large autism cohort. Koomar, T., Thomas, T. R., Pottschmidt, N. R., Lutter, M., & Michaelson, J. J. (2021). Frontiers in Psychiatry, 12.
  • Click here to a PDF of the findings.
• Drug repositioning in epilepsy reveals novel antiseizure candidates. Brueggeman, L. , Sturgeon, M. L., Martin, R. M., Grossbach, A. J., Nagahama, Y. , Zhang, A. , Howard, M. A., Kawasaki, H. , Wu, S. , Cornell, R. A., Michaelson, J. J. and Bassuk, A. G. (2019). Ann Clin Transl Neurol, 6: 295-309.
   
• Whole-genome sequencing in a family with twin boys with autism and intellectual disability suggests multimodal polygenic risk. Brooke McKenna, Tanner Koomar, Kevin Vervier, Jamie Kremsreiter, and Jacob J. Michaelson. Cold Spring Harb Mol Case Stud December 2018, 4: a003285
   
• TiSAn: estimating tissue-specific effects of coding and non-coding variants. Kévin Vervier, Jacob J Michaelson. Bioinformatics, Volume 34, Issue 18, 15 September 2018, Pages 3061–3068

• PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction. Dehzangi, A., López, Y., Lal, S.P., Taherzadeh, G., Michaelson, J., Sattar, A., Tsunoda, T. and Sharma, A. Journal of Theoretical Biology, 425, pp.97-102, July 2017.
   
• SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids. López, Y., Dehzangi, A., Lal, S.P., Taherzadeh, G., Michaelson, J., Sattar, A., Tsunoda, T. and Sharma, A. Analytical Biochemistry, 527, pp.24-32, June 2017.
   
• cerebroViz: An R package for anatomical visu-alization of spatiotemporal brain data. Bahl E, Koomar T, Michaelson JJ. Bioinformatics, pii: btw726, Dec 2016.
   
• SLINGER: large-scale learning for predicting gene expression. Vervier K and Michaelson JJ. Sci Rep, 6:39360, Dec 2016.
   
• Common Genetic Variants in FOXP2 Are Not Associated with Individual Differences in Language Development. Mueller KL, Murray JC, Michaelson JJ, Christiansen MH, Reilly S, Tomblin JB. PLoS One, 11(4):e0152576, Apr 2016.
   
• Adaptation of the targeted capture Methyl-Seq platform for the mouse genome identifies novel tissue-specific DNA methylation patterns of genes involved in neurodevelopment. Hing B, Ramos E, Braun P, McKane M, Jancic D, Tamashiro KL, Lee RS, Michaelson JJ, Druley TE, Potash JB. Epigenetics, 10(7):581-96, Jul 2015.
   
• Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism. Corominas R, Yang X, Lin GN, Kang S, Shen Y, Ghamsari L, Broly M, Rodriguez M, Tam S, Trigg SA, Fan C, Yi S, Tasan M, Lemmens I, Kuang X, Zhao N, Malhotra D, Michaelson JJ, Vacic V, Calderwood MA, Roth FP, Tavernier J, Horvath S, Salehi-Ashtiani K, Korkin D, Sebat J, Hill DE, Hao T, Vidal M, Iakoucheva LM. Nat Commun, 11;5:3650, Apr 2014.
   
• ​A complete mass spectrometric map for the analysis of the yeast proteome and its application to quantitative trait analysis. Picotti P, Clément-Ziza M, Lam H, Campbell DS, Schmidt A, Deutsch EW, Röst H, Sun Z, Rinner O, Reiter L, Shen Q, Michaelson JJ, Frei A, Alberti S, Kusebauch U, Wollscheid B, Moritz R, Beyer A, Aebersold R. Nature, 494(7436):266-70, Jan 2013.
   
• Whole genome sequencing in autism identifies hotspots for de novo germline mutation. Michaelson JJ, Shi Y, Gujral M, Zheng H, Malhotra D, Jin X, Minghan J, Greer D, Bhandari A, Wu W, Corominas R, Peoples Á, Koren A, Gore A, Kang S, Lin GN, Estabillo J, Gadomski T, Singh B, Zhang K, Akshoomoff N, Corsello C, McCarroll S, Iakoucheva LM, Li Y, Wang J, Sebat J. Cell, 151(7):1431-42, Dec 2012.
   
• Differential relationship of DNA replication timing to different forms of human mutation and variation. Koren A, Polak P, Nemesh J, Michaelson JJ, Sebat J, Sunyaev SR, McCarroll SA. Am J Hum Genet , 91(6):1033-40, Nov 2012.
   
• forestSV: structural variant discovery through statistical learning. Michaelson JJ, Sebat J. Nat Methods, 9(8):819-21, Jul 2012.
   
• Teamwork: improved eQTL mapping using combinations of machine learning methods. Ackermann M, Clément-Ziza M, Michaelson JJ, Beyer A. PLoS One, 77:e40916, 2012.
   
• High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Malhotra D, McCarthy S, Michaelson JJ, Vacic V, Burdick KE, Yoon S, Cichon S, Corvin A, Gary S, Gershon ES, Gill M, Karayiorgou M, Kelsoe JR, Krastoshevsky O, Krause V, Leibenluft E, Levy DL, Makarov V, Bhandari A, Malhotra AK, McMahon FJ, Nöthen MM, Potash JB, Rietschel M, Schulze TG, Sebat J. Neuron, 72(6):951-963, Dec 2011.
   
• DIGE-based protein expression analysis of BaP-exposed hepatoma cells reveals a complex stress response at toxic and subacute concentrations. Dautel F, Kalkhof S, Trump S, Michaelson JJ, Beyer A, Lehmann I, von Bergen M. J Proteome Res, Feb 2011.
   
• Transcriptional signatures of regulatory and toxic responses to benzo-[a]-pyrene exposure. Michaelson JJ, Trump S, Rudzok S, Gräbsch C, Madureira D, Dautel F, Schirmer K, von Bergen M, Lehmann I, Beyer A. BMC Genomics, 12:502, Oct 2011.
   
• Integrative analysis of low-and high-resolution eQTL. Loguercio S, Overall R, Michaelson JJ, Wiltshire T, Pletcher MT, Miller BH, Walker J, Kempermann G, Su A, Beyer A. PLoS ONE, 5(11):e13920, Nov 2010.
   
• Data-driven assessment of eQTL mapping methods. Michaelson JJ, Alberts R, Schughart K, Beyer A. BMC Genomics, 11:502, Sep 2010.
   
• Detection and interpretation of expression quantitative trait loci (eQTL). Michaelson JJ, Loguercio S, Beyer A. Methods, 48:265-276, 2009.
   
• Microarray analysis reveals potential mechanisms of BRMS1-mediated metastasis suppression. Champine PJ, Michaelson J, Weimer BC, Welch DR, DeWald DB. Clin Exp Metastasis, 24, 551-65, 2007.
   
• Identification of dehydration responsive genes from two non-nodulated 3 of 5 alfalfa cultivars using Medicago truncatula microarrays. Chen D, Liang M, DeWald D, Peel M, Bugbee B, Michaelson JJ, Davis E, Wu Y. Acta Physiologiae Plantarum, 30:183-199, 2007.